Enhanced Fault Detection and Diagnosis in Industrial Distillation Column Using Explainable Artificial Intelligence and Machine Learning
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Published
Nov 3, 2025
Sumana Roy
Somasish Saha
Bitopama Modak
Fahim Ahmed
Aditi Mahto
Sandip Kumar Lahiri
Abstract
This study presents a comprehensive methodology for developing a Fault Detection and Diagnosis (FDD) system for an industrial distillation column using advanced machine learning algorithms. Steady state and dynamic simulations in Aspen Plus® generate extensive datasets under normal and faulty conditions. Feature engineering, using the Minimum Redundancy Maximum Relevance (MRMR) algorithm, selects the most relevant features for fault detection. Various machine learning models, including Decision Trees, Support Vector Machines, k-nearest Neighbours, and Neural Networks, were trained and evaluated based on performance metrics such as accuracy, recall, precision, and F1 score.
The top models were integrated into a stacked classifier system with a voting mechanism to enhance fault detection reliability. Explainable Artificial Intelligence (XAI) techniques, such as Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), were incorporated to improve model interpretability, allowing engineers to understand and validate the FDD system's decision-making process.
Simulation results confirm that the proposed methodology accurately identifies and classifies faults. By integrating dynamic simulations, advanced machine learning, and XAI techniques, a robust and scalable solution is achieved for fault detection in distillation columns, improving operational reliability, safety, and reducing downtime.
Future work could extend this approach to other industrial processes and explore additional machine learning algorithms to further enhance performance.
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Keywords
Implemented Artificial Intelligence, Applications of Artificial Intelligence, Fault Detection and Diagnosis
References
Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175–185. doi.org/10.1080/00031305.1992.10475879
Amiruddin, A. A. A. M., Zabiri, H., Taqvi, S. A. A., & Tufa, L. D. (2020). Fault detection and diagnosis in chemical processes. Neural Computing and Applications, 32, 447–472. doi.org/10.1007/s00521-019-04167-8
Andre, A. B., Beltrame, E., & Wainer, J. (2013). Fault detection and diagnosis in chemical processes. Applied ArtificialIntelligence,27(1),3649.doi.org/10.1080/08839514.2013.748914
Aydin, I., Karakose, M., & Akin, E. (2014). Fault detection and diagnosis in chemical processes. ISA Transactions, 53(2),220–229.doi.org/10.1016/j.isatra.2013.10.007
Chetouani, Y. (2007). Fault detection and diagnosis in chemical processes. International Journal of Computer Science & Applications, 4(3), 119–133.
Chetouani, Y. (2011). Fault detection and diagnosis in chemical processes. Systems Engineering Procedia, 1, 473–480. doi.org/10.1016/j.sepro.2011.08.072
Chiang, L. H., & Pell, R. J. (2004). Fault detection and diagnosis in chemical processes. Journal of Process Control,14(2),143-155. doi.org/10.1016/j.jprocont.2003.09.003
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2000). Fault detection and diagnosis in industrial systems. Springer. doi.org/10.1007/978-1-4471-0409-4
Copertaro, E., Chiariotti, P., Revel, G. M., & Paone, N. (2019). Fault detection and diagnosis in chemical processes. Journal of Process Control, 80, 180–192. doi.org/10.1016/j.jprocont.2019.06.001
Dash, S., Rengaswamy, R., & Venkatasubramanian, V. (2003). Fuzzy-logic based trend classification for fault diagnosis of chemical processes. Computers & Chemical Engineering, 27(3), 347–362. doi.org/10.1016/S0098-1354(02)00214-4
Demetgul, M. (2013). Fault detection and diagnosis in chemical processes. International Journal of Advanced Manufacturing Technology, 67(9–12), 2183–2194. /doi.org/10.1007/s00170-012-4635-4
Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017). Fault detection and diagnosis in chemical processes. IEEE Access,5,2059020616. doi.org/10.1109/ACCESS.2017.2756879
Ge, Z., Song, Z., & Gao, F. (2013). Fault detection and diagnosis in chemical processes. Industrial & Engineering Chemistry Research, 52(10), 3543–3562. doi.org/10.1021/ie302071k
Ge, Z., Zhong, S., & Zhang, Y. (2016). Fault detection and diagnosis in chemical processes. IEEE Transactions on Industrial Informatics, 12(4), 1403–1411. doi.org/10.1109/TII.2016.2552539
Gonzaga, J. C. B., Meleiro, L. a. C., Kiang, C., & Maciel Filho, R. (2009). ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process. Computers & Chemical Engineering,33(1),43–49. doi.org/10.1016/j.compchemeng.2008.05.019
Hasan, M. J., & Kim, J.-M. (2019). Fault detection and diagnosis in chemical processes. Energies, 12(6), 991. doi.org/10.3390/en12060991
He, S.-G., He, Z., & Wang, G. A. (2013). Fault detection and diagnosis in chemical processes. Journal of Intelligent Manufacturing, 24(1), 25–34 . doi.org/10.1007/s10845-011-0541-4
Hoskins, J., Kaliyur, K., & Himmelblau, D. M. (1991). Fault detection and diagnosis in chemical processes. AIChE Journal,37(1), 137–141. doi.org/10.1002/aic.690370114
Hwang, B., Saif, M., & Jamshidi, M. (1993). Fault detection and diagnosis in chemical processes. IFAC Proceedings, 26(2), 547550. doi.org/10.1016/S14746670(17)47358-1
Iliyas, S. A., Elshafei, M., Habib, M. A., & Adeniran, A. A. (2013). Fault detection and diagnosis in chemical processes. Control Engineering Practice, 21(7), 962–970. doi.org/10.1016/j.conengprac.2013.03.004
Isermann, R. (2006). Fault-diagnosis systems: An introduction from fault detection to fault tolerance. Springer Science & Business Media. doi.org/10.1007/3-540-30368-5
Isermann, R., & Balle, P. (1997). Fault detection and diagnosis in chemical processes. Control Engineering Practice, 5(5), 709–719. doi.org/10.1016/S0967-0661(97)00053-1
Jamil, M., Kalam, A., Ansari, A., & Rizwan, M. (2014). Fault detection and diagnosis in chemical processes. Applied SoftComputing,19,322–332. doi.org/10.1016/j.asoc.2014.02.003
Jiang, L., Lei, X., & Shuqing, W. (2006). Fault detection and diagnosis in chemical processes. Chinese Journal of Chemical Engineering, 14(3), 343–348. doi.org/10.1016/S1004-9541(06)60072-8
Karabadji, N. E. I., Seridi, H., Khelf, I., Azizi, N., & Boulkroune, R. (2014). Fault detection and diagnosis in chemical processes. Engineering Applications of ArtificialIntelligence,35,71–83. doi.org/10.1016/j.engappai.2014.06.001
Kesavan, P., & Lee, J. H. (1997). Fault detection and diagnosis in chemical processes. Industrial & Engineering Chemistry Research, 36(7), 2725–2738. doi.org/10.1021/ie960768m
Kuo, Y., & Lin, K.-P. (2010). Fault detection and diagnosis in chemical processes. International Journal of Advanced Manufacturing Technology, 50(9–12), 1243–1251. doi.org/10.1007/s00170-010-2585-6
Lee, J. M., Yoo, C. K., Choi, S. W., Vanrolleghem, P. A., & Lee, I. B. (2004). Nonlinear process monitoring using kernel principal component analysis .Chemical Engineering Science, 59(1), 223–234. doi.org/10.1016/j.ces.2003.09.022
Lee, M. W., Joung, J. Y., Lee, D. S., Park, J. M., & Woo, S. H. (2005). Fault detection and diagnosis in chemical processes. Industrial & Engineering Chemistry Research, 44(11), 3973–3982. doi.org/10.1021/ie0489276
Li, W., Yue, H. H., Valle, S., Qin, S. J., & McAvoy, T. J. (2000). Recursive PCA for adaptive process monitoring. Journal of Process Control, 10(5) ,471–486. doi.org/10.1016/S09591524(00)00022-6
Li, Y., & Zhang, X. (2014). Fault detection and diagnosis in chemical processes. Chemometrics and Intelligent Laboratory Systems, 136, 47–57. doi.org/10.1016/j.chemolab.2014.04.005
Liu, D., Shang, J., & Chen, M. (2020). Fault detection and diagnosis in chemical processes. IEEE Transactions on Industrial Informatics. doi.org/10.1109/TII.2020.3031496
Liu, T.-I., & Jolley, B. (2015). Fault detection and diagnosis in chemical processes. International Journal of Advanced Manufacturing Technology, 78(9–12), 1999–2007. doi.org/10.1007/s00170-014-6765-7
Lu, W.-Z., & Xue, Y. (2014). Fault detection and diagnosis in chemical processes. Building and Environment, 78, 111–117. doi.org/10.1016/j.buildenv.2014.04.014
Ma, C. Y., & Wang, X. Z. (2009). Fault detection and diagnosis in chemical processes. Computers & Chemical Engineering, 33(10), 1602–1616. doi.org/10.1016/j.compchemeng.2009.05.004
MacGregor, J. F., & Kourti, T. (1995). Statistical process control of multivariate processes. Control Engineering Practice, 3(3), 403–414. doi.org/10.1016/0967-0661(95)00014-L
Manssouri, I., Chetouani, Y., & Kihel, B. E. (2008). Fault detection and diagnosis in chemical processes. International Journal of Computer Applications in Technology, 32(3), 181–186. doi.org/10.1504/IJCAT.2008.020953
Maurya, M. R., Rengaswamy, R., & Venkatasubramanian, V. (2007). Fault diagnosis using dynamic simulation and neural networks. Journal of Process Control, 17(3), 272–289. doi.org/10.1016/j.jprocont.2006.10.004
Meng, Y., Lan, Q., Qin, J., Yu, S., Pang, H., & Zheng, K. (2019). Fault detection and diagnosis in chemical processes .Journalof Food Engineering, 241, 159165. doi.org/10.1016/j.jfoodeng.2018.08.022
Molnar,C.(2019).InterpretableMachine Learning. Lulu.com.
Mujtaba, S. M., Lemma, T. A., Taqvi, S. A. A., Ofei, T. N., & Vandrangi, S. K. (2020). Fault detection and diagnosis in chemical processes. Processes, 8(4), 474. doi.org/10.3390/pr8040474
Nagpal, T., & Brar, Y. S. (2014). Fault detection and diagnosis in chemical processes. Neural Computing and Applications, 25(7–8), 1863–1870. /doi.org/10.1007/s00521-014-1658-3
Nawaz, M., Maulud, A. S., Zabiri, H., Taqvi, S. A. A., & Idris, A. (2020). Fault detection and diagnosis in chemical processes. Chinese Journal of Chemical Engineering. doi.org/10.1016/j.cjche.2020.08.035
Nguyen, N.-T., & Lee, H.-H. (2010). Fault detection and diagnosis in chemical processes. International Journal of Electronics, 97(9), 1071–1081. doi.org/10.1080/00207217.2010.496673
Nor, N. M., Hassan, C. R. C., & Hussain, M. A. (2020). Fault detection and diagnosis in chemical processes. Reviews in Chemical Engineering, 36(4), 513–553. doi.org/10.1515/revce-2019-0052
Nozari, H. A., Shoorehdeli, M. A., Simani, S., & Banadaki, H. D. (2012). Fault detection and diagnosis in chemical processes .Neuro computing, 91, 29–47. doi.org/10.1016/j.neucom.2012.02.016
Pani, A. K., & Mohanta, H. K. (2015). Fault detection and diagnosis in chemical processes. ISA Transactions, 56, 206–221. doi.org/10.1016/j.isatra.2014.10.004
Quinlan, J. R. (1979). In Expert Systems in the Micro Electronics Age. Edinburgh University Press.
Quinlan, J. R. (2014). C4.5: Programs for Machine Learning. Elsevier.
Rajakarunakaran, S., Venkumar, P., Devaraj, D., & Rao, K. S. P. (2008). Fault detection and diagnosis in chemical processes. Applied Soft Computing, 8(1), 740–748. doi.org/10.1016/j.asoc.2007.07.002
Rengaswamy, R., & Venkatasubramanian, V. (2001). A syntactic pattern-recognition approach for process monitoring and fault diagnosis. Engineering Applications of Artificial Intelligence, 14(6), 785–796. doi.org/10.1016/S0952-1976(01)00030-8
Schubert, U., Kruger, U., Arellano-Garcia, H., de S. á Feital, T., & Wozny, G. (2011). Fault detection and diagnosis in chemicalprocesses.Control Engineering Practice, 19(5),479–490. doi.org/10.1016/j.conengprac.2011.02.002
Shaik, N. B., Pedapati, S. R., Taqvi, S. A. A., Othman, A., & Dzubir, F. A. A. (2020). Fault detection and diagnosis in chemicalprocesses.Processes,8(6),661. doi.org/10.3390/pr8060661
Sharma, R., Singh, K., Singhal, D., & Ghosh, R. (2004). Fault detection and diagnosis in chemical processes. Chemical Engineering and Processing: Process Intensification, 43(7), 841847. doi.org/10.1016/S02552701(03)00103-X
Taqvi, S. A. A., Zabiri, H., Tufa, L. D., Fatima, S. A., & Maulud, A. H. S. (2020). Distillation column: Review of major disturbances. Proceedings of the 9th IEEE International Conference on Control System, Computing andEngineering,1–6. doi.org/10.1109/ICCSCE50387.2020.9274987
Taqvi, S. A. A., Zabiri, H., Tufa, L. D., Uddin, F., Fatima, S. A., & Maulud, A. S.(2020). Fault detection and diagnosis in chemical processes. Jurnal Teknologi, 82(5), 14350. doi.org/10.11113/jt.v82.14350
Taqvi, S. A., Tufa, L. D., Zabiri, H., Mahadzir, S., Maulud, A. S., & Uddin, F. (2017). Artificial neural network for anomalies detection in distillation column. In D. Modeling & S. of Systems Springer (Eds.), Singapore (pp. 447–472). Springer. doi.org/10.1007/978-981-10-6463-0\_26
Taqvi, S. A., Tufa, L. D., Zabiri, H., Maulud, A. S., & Uddin, F. (2018a). Fault detection and diagnosis in chemical processes. Neural Computing and Applications, 32, 3503–3519. doi.org/10.1007/s00521-018-3593-7
Taqvi, S. A., Tufa, L. D., Zabiri, H., Maulud, A. S., & Uddin, F.(2018b).Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine. Industrial and Engineering Chemistry Research, 57(43), 14689–14706. doi.org/10.1021/acs.iecr.8b03360
Tidriri, K., Chatti, N., Verron, S., & Tiplica, T. (2016). Fault detection and diagnosis in chemical processes. Annual Reviews in Control, 42, 63–81. doi.org/10.1016/j.arcontrol.2016.09.001
Tudón-Martínez, J. C., & Morales-Menendez, R. (2015). Fault detection and diagnosis in chemical processes. IFAC-PapersOnLine,48(21),1210–1215. doi.org/10.1016/j.ifacol.2015.09.372
Ungar, L., Powell, B., & Kamens, S. (1990). Fault detection and diagnosis in chemical processes. Computers & ChemicalEngineering,14(4–5),561–572. /doi.org/10.1016/0098-1354(90)87045-8
Vedam, H., Dash, S., & Venkatasubramanian, V. (1999). Fault detection and diagnosis in chemical processes. Computers & Chemical Engineering, 23, S577–S580. doi.org/10.1016/S0098-1354(99)80097-1
Venkatasubramanian, V., & Chan, K. (1989). Fault detection and diagnosis in chemical processes. AIChE Journal, 35(12), 1993–2002. doi.org/10.1002/aic.690351214
Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies. Computers & Chemical Engineering, 27(3), 313–326. doi.org/10.1016/S0098-1354(02)00161-8
Venkatasubramanian, V., Rengaswamy, R., & Yin, K. (2003). A review of process fault detection and diagnosis Part I : Quantitative model-based methods. 27, 293–311.
Watanabe, K., Matsuura, I., Abe, M., Kubota, M., & Himmelblau, D. (1989). Fault detection and diagnosis in chemical processes. AIChE Journal, 35(11), 1803–1812. doi.org/10.1002/aic.690351111
Weber, P., Theilliol, D., Aubrun, C., & Evsukoff, A. (2006). Fault detection and diagnosis in chemical processes. IFAC Proceedings, 39(13), 90–95. doi.org/10.3182/20060821-3-FR-2920.00015
Xu, S., & Liu, X. (2014). Fault detection and diagnosis in chemical processes. Neurocomputing, 142, 291–298. doi.org/10.1016/j.neucom.2014.04.046
Xu, Z., Weiwu, Y., & Huihe, S. (2006). Fault detection and diagnosis in chemical processes. Chinese Journal of Chemical Engineering, 14(6), 759–764. doi.org/10.1016/S1004-9541(07)60007-1
Yu, J., & Zhang, C. (2020). Fault detection and diagnosis in chemical processes. Journal of Process Control, 92, 119–136. doi.org/10.1016/j.jprocont.2020.03.001
Zhang, J., Li, J., & Wang, Y. (2019). A review on the research and applications of machine learning in fault diagnosis and detection for chemical process. Processes, 7(12), 839. doi.org/10.3390/pr7120839
Zhang, S., Zhao, C., Wang, S., & Wang, F. (2017). Fault detection and diagnosis in chemical processes. Industrial & Engineering Chemistry Research, 56(3), 728–740. doi.org/10.1021/acs.iecr.6b03845
Zhang, X., & Hoo, K. A. (2011). Fault detection and diagnosis in chemical processes. Computers & Chemical Engineering,35(1),132–148. doi.org/10.1016/j.compchemeng.2010.09.004
Zhang, X., Yan, W. W., Zhao, X., & Shao, H. H. (2007). Fault detection and diagnosis in chemical processes. Chemical Engineering & Technology, 30(9), 1203–1211. doi.org/10.1002/ceat.200700140
Zhao, Y., Xiao, F., & Wang, S. (2013). Fault detection and diagnosis in chemical processes. Energy and Buildings, 57, 278–288. doi.org/10.1016/j.enbuild.2012.11.008
Zhou, J., Guo, A., Celler, B., & Su, S. (2014). Fault detection and diagnosis in chemical processes. Applied Soft Computing,14,4–11. doi.org/10.1016/j.asoc.2013.12.001
Zhu, Z.-B., & Song, Z.-H. (2010). Fault detection and diagnosis in chemical processes. Chemical Engineering Research and Design, 88(8), 936–951. doi.org/10.1016/j.cherd.2010.01.019
Zhu, Z.-B., & Song, Z.-H. (2011). Fault detection and diagnosis in chemical processes. Expert Systems with Applications,38(6),6895–6905. doi.org/10.1016/j.eswa.2010.12.038
Zio, E., Baraldi, P., & Popescu, I. C. (2009). Fault detection and diagnosis in chemical processes. Annals of Nuclear Energy, 36(8),1159–1169. doi.org/10.1016/j.anucene.2009.03.004
Amiruddin, A. A. A. M., Zabiri, H., Taqvi, S. A. A., & Tufa, L. D. (2020). Fault detection and diagnosis in chemical processes. Neural Computing and Applications, 32, 447–472. doi.org/10.1007/s00521-019-04167-8
Andre, A. B., Beltrame, E., & Wainer, J. (2013). Fault detection and diagnosis in chemical processes. Applied ArtificialIntelligence,27(1),3649.doi.org/10.1080/08839514.2013.748914
Aydin, I., Karakose, M., & Akin, E. (2014). Fault detection and diagnosis in chemical processes. ISA Transactions, 53(2),220–229.doi.org/10.1016/j.isatra.2013.10.007
Chetouani, Y. (2007). Fault detection and diagnosis in chemical processes. International Journal of Computer Science & Applications, 4(3), 119–133.
Chetouani, Y. (2011). Fault detection and diagnosis in chemical processes. Systems Engineering Procedia, 1, 473–480. doi.org/10.1016/j.sepro.2011.08.072
Chiang, L. H., & Pell, R. J. (2004). Fault detection and diagnosis in chemical processes. Journal of Process Control,14(2),143-155. doi.org/10.1016/j.jprocont.2003.09.003
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2000). Fault detection and diagnosis in industrial systems. Springer. doi.org/10.1007/978-1-4471-0409-4
Copertaro, E., Chiariotti, P., Revel, G. M., & Paone, N. (2019). Fault detection and diagnosis in chemical processes. Journal of Process Control, 80, 180–192. doi.org/10.1016/j.jprocont.2019.06.001
Dash, S., Rengaswamy, R., & Venkatasubramanian, V. (2003). Fuzzy-logic based trend classification for fault diagnosis of chemical processes. Computers & Chemical Engineering, 27(3), 347–362. doi.org/10.1016/S0098-1354(02)00214-4
Demetgul, M. (2013). Fault detection and diagnosis in chemical processes. International Journal of Advanced Manufacturing Technology, 67(9–12), 2183–2194. /doi.org/10.1007/s00170-012-4635-4
Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017). Fault detection and diagnosis in chemical processes. IEEE Access,5,2059020616. doi.org/10.1109/ACCESS.2017.2756879
Ge, Z., Song, Z., & Gao, F. (2013). Fault detection and diagnosis in chemical processes. Industrial & Engineering Chemistry Research, 52(10), 3543–3562. doi.org/10.1021/ie302071k
Ge, Z., Zhong, S., & Zhang, Y. (2016). Fault detection and diagnosis in chemical processes. IEEE Transactions on Industrial Informatics, 12(4), 1403–1411. doi.org/10.1109/TII.2016.2552539
Gonzaga, J. C. B., Meleiro, L. a. C., Kiang, C., & Maciel Filho, R. (2009). ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process. Computers & Chemical Engineering,33(1),43–49. doi.org/10.1016/j.compchemeng.2008.05.019
Hasan, M. J., & Kim, J.-M. (2019). Fault detection and diagnosis in chemical processes. Energies, 12(6), 991. doi.org/10.3390/en12060991
He, S.-G., He, Z., & Wang, G. A. (2013). Fault detection and diagnosis in chemical processes. Journal of Intelligent Manufacturing, 24(1), 25–34 . doi.org/10.1007/s10845-011-0541-4
Hoskins, J., Kaliyur, K., & Himmelblau, D. M. (1991). Fault detection and diagnosis in chemical processes. AIChE Journal,37(1), 137–141. doi.org/10.1002/aic.690370114
Hwang, B., Saif, M., & Jamshidi, M. (1993). Fault detection and diagnosis in chemical processes. IFAC Proceedings, 26(2), 547550. doi.org/10.1016/S14746670(17)47358-1
Iliyas, S. A., Elshafei, M., Habib, M. A., & Adeniran, A. A. (2013). Fault detection and diagnosis in chemical processes. Control Engineering Practice, 21(7), 962–970. doi.org/10.1016/j.conengprac.2013.03.004
Isermann, R. (2006). Fault-diagnosis systems: An introduction from fault detection to fault tolerance. Springer Science & Business Media. doi.org/10.1007/3-540-30368-5
Isermann, R., & Balle, P. (1997). Fault detection and diagnosis in chemical processes. Control Engineering Practice, 5(5), 709–719. doi.org/10.1016/S0967-0661(97)00053-1
Jamil, M., Kalam, A., Ansari, A., & Rizwan, M. (2014). Fault detection and diagnosis in chemical processes. Applied SoftComputing,19,322–332. doi.org/10.1016/j.asoc.2014.02.003
Jiang, L., Lei, X., & Shuqing, W. (2006). Fault detection and diagnosis in chemical processes. Chinese Journal of Chemical Engineering, 14(3), 343–348. doi.org/10.1016/S1004-9541(06)60072-8
Karabadji, N. E. I., Seridi, H., Khelf, I., Azizi, N., & Boulkroune, R. (2014). Fault detection and diagnosis in chemical processes. Engineering Applications of ArtificialIntelligence,35,71–83. doi.org/10.1016/j.engappai.2014.06.001
Kesavan, P., & Lee, J. H. (1997). Fault detection and diagnosis in chemical processes. Industrial & Engineering Chemistry Research, 36(7), 2725–2738. doi.org/10.1021/ie960768m
Kuo, Y., & Lin, K.-P. (2010). Fault detection and diagnosis in chemical processes. International Journal of Advanced Manufacturing Technology, 50(9–12), 1243–1251. doi.org/10.1007/s00170-010-2585-6
Lee, J. M., Yoo, C. K., Choi, S. W., Vanrolleghem, P. A., & Lee, I. B. (2004). Nonlinear process monitoring using kernel principal component analysis .Chemical Engineering Science, 59(1), 223–234. doi.org/10.1016/j.ces.2003.09.022
Lee, M. W., Joung, J. Y., Lee, D. S., Park, J. M., & Woo, S. H. (2005). Fault detection and diagnosis in chemical processes. Industrial & Engineering Chemistry Research, 44(11), 3973–3982. doi.org/10.1021/ie0489276
Li, W., Yue, H. H., Valle, S., Qin, S. J., & McAvoy, T. J. (2000). Recursive PCA for adaptive process monitoring. Journal of Process Control, 10(5) ,471–486. doi.org/10.1016/S09591524(00)00022-6
Li, Y., & Zhang, X. (2014). Fault detection and diagnosis in chemical processes. Chemometrics and Intelligent Laboratory Systems, 136, 47–57. doi.org/10.1016/j.chemolab.2014.04.005
Liu, D., Shang, J., & Chen, M. (2020). Fault detection and diagnosis in chemical processes. IEEE Transactions on Industrial Informatics. doi.org/10.1109/TII.2020.3031496
Liu, T.-I., & Jolley, B. (2015). Fault detection and diagnosis in chemical processes. International Journal of Advanced Manufacturing Technology, 78(9–12), 1999–2007. doi.org/10.1007/s00170-014-6765-7
Lu, W.-Z., & Xue, Y. (2014). Fault detection and diagnosis in chemical processes. Building and Environment, 78, 111–117. doi.org/10.1016/j.buildenv.2014.04.014
Ma, C. Y., & Wang, X. Z. (2009). Fault detection and diagnosis in chemical processes. Computers & Chemical Engineering, 33(10), 1602–1616. doi.org/10.1016/j.compchemeng.2009.05.004
MacGregor, J. F., & Kourti, T. (1995). Statistical process control of multivariate processes. Control Engineering Practice, 3(3), 403–414. doi.org/10.1016/0967-0661(95)00014-L
Manssouri, I., Chetouani, Y., & Kihel, B. E. (2008). Fault detection and diagnosis in chemical processes. International Journal of Computer Applications in Technology, 32(3), 181–186. doi.org/10.1504/IJCAT.2008.020953
Maurya, M. R., Rengaswamy, R., & Venkatasubramanian, V. (2007). Fault diagnosis using dynamic simulation and neural networks. Journal of Process Control, 17(3), 272–289. doi.org/10.1016/j.jprocont.2006.10.004
Meng, Y., Lan, Q., Qin, J., Yu, S., Pang, H., & Zheng, K. (2019). Fault detection and diagnosis in chemical processes .Journalof Food Engineering, 241, 159165. doi.org/10.1016/j.jfoodeng.2018.08.022
Molnar,C.(2019).InterpretableMachine Learning. Lulu.com.
Mujtaba, S. M., Lemma, T. A., Taqvi, S. A. A., Ofei, T. N., & Vandrangi, S. K. (2020). Fault detection and diagnosis in chemical processes. Processes, 8(4), 474. doi.org/10.3390/pr8040474
Nagpal, T., & Brar, Y. S. (2014). Fault detection and diagnosis in chemical processes. Neural Computing and Applications, 25(7–8), 1863–1870. /doi.org/10.1007/s00521-014-1658-3
Nawaz, M., Maulud, A. S., Zabiri, H., Taqvi, S. A. A., & Idris, A. (2020). Fault detection and diagnosis in chemical processes. Chinese Journal of Chemical Engineering. doi.org/10.1016/j.cjche.2020.08.035
Nguyen, N.-T., & Lee, H.-H. (2010). Fault detection and diagnosis in chemical processes. International Journal of Electronics, 97(9), 1071–1081. doi.org/10.1080/00207217.2010.496673
Nor, N. M., Hassan, C. R. C., & Hussain, M. A. (2020). Fault detection and diagnosis in chemical processes. Reviews in Chemical Engineering, 36(4), 513–553. doi.org/10.1515/revce-2019-0052
Nozari, H. A., Shoorehdeli, M. A., Simani, S., & Banadaki, H. D. (2012). Fault detection and diagnosis in chemical processes .Neuro computing, 91, 29–47. doi.org/10.1016/j.neucom.2012.02.016
Pani, A. K., & Mohanta, H. K. (2015). Fault detection and diagnosis in chemical processes. ISA Transactions, 56, 206–221. doi.org/10.1016/j.isatra.2014.10.004
Quinlan, J. R. (1979). In Expert Systems in the Micro Electronics Age. Edinburgh University Press.
Quinlan, J. R. (2014). C4.5: Programs for Machine Learning. Elsevier.
Rajakarunakaran, S., Venkumar, P., Devaraj, D., & Rao, K. S. P. (2008). Fault detection and diagnosis in chemical processes. Applied Soft Computing, 8(1), 740–748. doi.org/10.1016/j.asoc.2007.07.002
Rengaswamy, R., & Venkatasubramanian, V. (2001). A syntactic pattern-recognition approach for process monitoring and fault diagnosis. Engineering Applications of Artificial Intelligence, 14(6), 785–796. doi.org/10.1016/S0952-1976(01)00030-8
Schubert, U., Kruger, U., Arellano-Garcia, H., de S. á Feital, T., & Wozny, G. (2011). Fault detection and diagnosis in chemicalprocesses.Control Engineering Practice, 19(5),479–490. doi.org/10.1016/j.conengprac.2011.02.002
Shaik, N. B., Pedapati, S. R., Taqvi, S. A. A., Othman, A., & Dzubir, F. A. A. (2020). Fault detection and diagnosis in chemicalprocesses.Processes,8(6),661. doi.org/10.3390/pr8060661
Sharma, R., Singh, K., Singhal, D., & Ghosh, R. (2004). Fault detection and diagnosis in chemical processes. Chemical Engineering and Processing: Process Intensification, 43(7), 841847. doi.org/10.1016/S02552701(03)00103-X
Taqvi, S. A. A., Zabiri, H., Tufa, L. D., Fatima, S. A., & Maulud, A. H. S. (2020). Distillation column: Review of major disturbances. Proceedings of the 9th IEEE International Conference on Control System, Computing andEngineering,1–6. doi.org/10.1109/ICCSCE50387.2020.9274987
Taqvi, S. A. A., Zabiri, H., Tufa, L. D., Uddin, F., Fatima, S. A., & Maulud, A. S.(2020). Fault detection and diagnosis in chemical processes. Jurnal Teknologi, 82(5), 14350. doi.org/10.11113/jt.v82.14350
Taqvi, S. A., Tufa, L. D., Zabiri, H., Mahadzir, S., Maulud, A. S., & Uddin, F. (2017). Artificial neural network for anomalies detection in distillation column. In D. Modeling & S. of Systems Springer (Eds.), Singapore (pp. 447–472). Springer. doi.org/10.1007/978-981-10-6463-0\_26
Taqvi, S. A., Tufa, L. D., Zabiri, H., Maulud, A. S., & Uddin, F. (2018a). Fault detection and diagnosis in chemical processes. Neural Computing and Applications, 32, 3503–3519. doi.org/10.1007/s00521-018-3593-7
Taqvi, S. A., Tufa, L. D., Zabiri, H., Maulud, A. S., & Uddin, F.(2018b).Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine. Industrial and Engineering Chemistry Research, 57(43), 14689–14706. doi.org/10.1021/acs.iecr.8b03360
Tidriri, K., Chatti, N., Verron, S., & Tiplica, T. (2016). Fault detection and diagnosis in chemical processes. Annual Reviews in Control, 42, 63–81. doi.org/10.1016/j.arcontrol.2016.09.001
Tudón-Martínez, J. C., & Morales-Menendez, R. (2015). Fault detection and diagnosis in chemical processes. IFAC-PapersOnLine,48(21),1210–1215. doi.org/10.1016/j.ifacol.2015.09.372
Ungar, L., Powell, B., & Kamens, S. (1990). Fault detection and diagnosis in chemical processes. Computers & ChemicalEngineering,14(4–5),561–572. /doi.org/10.1016/0098-1354(90)87045-8
Vedam, H., Dash, S., & Venkatasubramanian, V. (1999). Fault detection and diagnosis in chemical processes. Computers & Chemical Engineering, 23, S577–S580. doi.org/10.1016/S0098-1354(99)80097-1
Venkatasubramanian, V., & Chan, K. (1989). Fault detection and diagnosis in chemical processes. AIChE Journal, 35(12), 1993–2002. doi.org/10.1002/aic.690351214
Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies. Computers & Chemical Engineering, 27(3), 313–326. doi.org/10.1016/S0098-1354(02)00161-8
Venkatasubramanian, V., Rengaswamy, R., & Yin, K. (2003). A review of process fault detection and diagnosis Part I : Quantitative model-based methods. 27, 293–311.
Watanabe, K., Matsuura, I., Abe, M., Kubota, M., & Himmelblau, D. (1989). Fault detection and diagnosis in chemical processes. AIChE Journal, 35(11), 1803–1812. doi.org/10.1002/aic.690351111
Weber, P., Theilliol, D., Aubrun, C., & Evsukoff, A. (2006). Fault detection and diagnosis in chemical processes. IFAC Proceedings, 39(13), 90–95. doi.org/10.3182/20060821-3-FR-2920.00015
Xu, S., & Liu, X. (2014). Fault detection and diagnosis in chemical processes. Neurocomputing, 142, 291–298. doi.org/10.1016/j.neucom.2014.04.046
Xu, Z., Weiwu, Y., & Huihe, S. (2006). Fault detection and diagnosis in chemical processes. Chinese Journal of Chemical Engineering, 14(6), 759–764. doi.org/10.1016/S1004-9541(07)60007-1
Yu, J., & Zhang, C. (2020). Fault detection and diagnosis in chemical processes. Journal of Process Control, 92, 119–136. doi.org/10.1016/j.jprocont.2020.03.001
Zhang, J., Li, J., & Wang, Y. (2019). A review on the research and applications of machine learning in fault diagnosis and detection for chemical process. Processes, 7(12), 839. doi.org/10.3390/pr7120839
Zhang, S., Zhao, C., Wang, S., & Wang, F. (2017). Fault detection and diagnosis in chemical processes. Industrial & Engineering Chemistry Research, 56(3), 728–740. doi.org/10.1021/acs.iecr.6b03845
Zhang, X., & Hoo, K. A. (2011). Fault detection and diagnosis in chemical processes. Computers & Chemical Engineering,35(1),132–148. doi.org/10.1016/j.compchemeng.2010.09.004
Zhang, X., Yan, W. W., Zhao, X., & Shao, H. H. (2007). Fault detection and diagnosis in chemical processes. Chemical Engineering & Technology, 30(9), 1203–1211. doi.org/10.1002/ceat.200700140
Zhao, Y., Xiao, F., & Wang, S. (2013). Fault detection and diagnosis in chemical processes. Energy and Buildings, 57, 278–288. doi.org/10.1016/j.enbuild.2012.11.008
Zhou, J., Guo, A., Celler, B., & Su, S. (2014). Fault detection and diagnosis in chemical processes. Applied Soft Computing,14,4–11. doi.org/10.1016/j.asoc.2013.12.001
Zhu, Z.-B., & Song, Z.-H. (2010). Fault detection and diagnosis in chemical processes. Chemical Engineering Research and Design, 88(8), 936–951. doi.org/10.1016/j.cherd.2010.01.019
Zhu, Z.-B., & Song, Z.-H. (2011). Fault detection and diagnosis in chemical processes. Expert Systems with Applications,38(6),6895–6905. doi.org/10.1016/j.eswa.2010.12.038
Zio, E., Baraldi, P., & Popescu, I. C. (2009). Fault detection and diagnosis in chemical processes. Annals of Nuclear Energy, 36(8),1159–1169. doi.org/10.1016/j.anucene.2009.03.004
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